Creating an Open Source Fraud Risk Management System in Order to Close the Financial Inclusion Gap

The presentation discusses the development of an open-source fraud risk management system center of excellence that benefits fintech, central banks, governments, merchants, and users. The system uses a semi-attached model and leverages data preparation, transaction evaluation, and topology to detect and prevent fraud.

• The presentation discusses the development of an open-source fraud risk management system center of excellence
• The system benefits fintech, central banks, governments, merchants, and users
• The system uses a semi-attached model
• Data preparation is crucial for making good decisions
• Transaction evaluation and topology are used to detect and prevent fraud
• The system sends information into a JSON file which can be used in a case management system
• The system works with regulators to investigate fraud
• A scenario is presented to illustrate how the system can detect a scam

The presentation provides an example scenario where multiple transactions are made through a central hub, and the system is able to detect a scam by analyzing the transaction amounts and cashing out to different people.

Fraud is expensive for everyone, but seriously detrimental to those in the financial inclusion gap. Fraud Risk Management Systems are expensive, so, they aren’t used where they are needed the most. Can a reliable and safe Fraud Risk Management system be built entirely with open source products? Can the solution itself be open source? This talk will discuss: - The scale up and scale down challenge - How do you build an open source system that stops fraud, money laundering and terrorist financing and not expose to fraudsters how to defeat the system? - Recommended Use Cases • Algorithmic system not an AI black box • The stack and architectural design. • Brief recorded demo. • Performance Optimizations.